
import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						Any:
				def get_masked_lm_array(lowerCamelCase    ):
								UpperCamelCase_:				Any						=    F'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
								UpperCamelCase_:				Dict						=    tf.train.load_variable(lowerCamelCase  ,     lowerCamelCase    )

								if "kernel" in name:
												UpperCamelCase_:				Any						=    array.transpose()

								return torch.from_numpy(lowerCamelCase    )

				def get_encoder_array(lowerCamelCase    ):
								UpperCamelCase_:				List[Any]						=    F'''encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
								UpperCamelCase_:				Optional[int]						=    tf.train.load_variable(lowerCamelCase  ,     lowerCamelCase    )

								if "kernel" in name:
												UpperCamelCase_:				Union[str, Any]						=    array.transpose()

								return torch.from_numpy(lowerCamelCase    )

				def get_encoder_layer_array(lowerCamelCase  ,     lowerCamelCase    ):
								UpperCamelCase_:				List[Any]						=    F'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
								UpperCamelCase_:				Union[str, Any]						=    tf.train.load_variable(lowerCamelCase  ,     lowerCamelCase    )

								if "kernel" in name:
												UpperCamelCase_:				Optional[int]						=    array.transpose()

								return torch.from_numpy(lowerCamelCase    )

				def get_encoder_attention_layer_array(lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    ):
								UpperCamelCase_:				int						=    F'''encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
								UpperCamelCase_:				str						=    tf.train.load_variable(lowerCamelCase  ,     lowerCamelCase    )
								UpperCamelCase_:				Tuple						=    array.reshape(lowerCamelCase    )

								if "kernel" in name:
												UpperCamelCase_:				Any						=    array.transpose()

								return torch.from_numpy(lowerCamelCase    )

				print(F'''Loading model based on config from {config_path}...'''    )
				UpperCamelCase_:				Optional[int]						=    BertConfig.from_json_file(lowerCamelCase    )
				UpperCamelCase_:				Optional[int]						=    BertForMaskedLM(lowerCamelCase    )

				# Layers
				for layer_index in range(0  ,     config.num_hidden_layers    ):
								UpperCamelCase_:				BertLayer						=    model.bert.encoder.layer[layer_index]

								# Self-attention
								UpperCamelCase_:				BertSelfAttention						=    layer.attention.self

								UpperCamelCase_:				Optional[int]						=    get_encoder_attention_layer_array(
								    lowerCamelCase  ,     """_query_dense/kernel"""  ,     self_attn.query.weight.data.shape    )
								UpperCamelCase_:				int						=    get_encoder_attention_layer_array(
								    lowerCamelCase  ,     """_query_dense/bias"""  ,     self_attn.query.bias.data.shape    )
								UpperCamelCase_:				Any						=    get_encoder_attention_layer_array(
								    lowerCamelCase  ,     """_key_dense/kernel"""  ,     self_attn.key.weight.data.shape    )
								UpperCamelCase_:				List[Any]						=    get_encoder_attention_layer_array(
								    lowerCamelCase  ,     """_key_dense/bias"""  ,     self_attn.key.bias.data.shape    )
								UpperCamelCase_:				int						=    get_encoder_attention_layer_array(
								    lowerCamelCase  ,     """_value_dense/kernel"""  ,     self_attn.value.weight.data.shape    )
								UpperCamelCase_:				Union[str, Any]						=    get_encoder_attention_layer_array(
								    lowerCamelCase  ,     """_value_dense/bias"""  ,     self_attn.value.bias.data.shape    )

								# Self-attention Output
								UpperCamelCase_:				BertSelfOutput						=    layer.attention.output

								UpperCamelCase_:				Optional[int]						=    get_encoder_attention_layer_array(
								    lowerCamelCase  ,     """_output_dense/kernel"""  ,     self_output.dense.weight.data.shape    )
								UpperCamelCase_:				int						=    get_encoder_attention_layer_array(
								    lowerCamelCase  ,     """_output_dense/bias"""  ,     self_output.dense.bias.data.shape    )

								UpperCamelCase_:				List[str]						=    get_encoder_layer_array(lowerCamelCase  ,     """_attention_layer_norm/gamma"""    )
								UpperCamelCase_:				str						=    get_encoder_layer_array(lowerCamelCase  ,     """_attention_layer_norm/beta"""    )

								# Intermediate
								UpperCamelCase_:				BertIntermediate						=    layer.intermediate

								UpperCamelCase_:				int						=    get_encoder_layer_array(lowerCamelCase  ,     """_intermediate_dense/kernel"""    )
								UpperCamelCase_:				str						=    get_encoder_layer_array(lowerCamelCase  ,     """_intermediate_dense/bias"""    )

								# Output
								UpperCamelCase_:				BertOutput						=    layer.output

								UpperCamelCase_:				Union[str, Any]						=    get_encoder_layer_array(lowerCamelCase  ,     """_output_dense/kernel"""    )
								UpperCamelCase_:				Optional[Any]						=    get_encoder_layer_array(lowerCamelCase  ,     """_output_dense/bias"""    )

								UpperCamelCase_:				Dict						=    get_encoder_layer_array(lowerCamelCase  ,     """_output_layer_norm/gamma"""    )
								UpperCamelCase_:				Union[str, Any]						=    get_encoder_layer_array(lowerCamelCase  ,     """_output_layer_norm/beta"""    )

				# Embeddings
				UpperCamelCase_:				Optional[int]						=    get_encoder_array("""_position_embedding_layer/embeddings"""    )
				UpperCamelCase_:				Dict						=    get_encoder_array("""_type_embedding_layer/embeddings"""    )
				UpperCamelCase_:				Dict						=    get_encoder_array("""_embedding_norm_layer/gamma"""    )
				UpperCamelCase_:				int						=    get_encoder_array("""_embedding_norm_layer/beta"""    )

				# LM Head
				UpperCamelCase_:				Optional[int]						=    model.cls.predictions.transform

				UpperCamelCase_:				Optional[int]						=    get_masked_lm_array("""dense/kernel"""    )
				UpperCamelCase_:				str						=    get_masked_lm_array("""dense/bias"""    )

				UpperCamelCase_:				Optional[Any]						=    get_masked_lm_array("""layer_norm/gamma"""    )
				UpperCamelCase_:				Optional[Any]						=    get_masked_lm_array("""layer_norm/beta"""    )

				UpperCamelCase_:				Any						=    get_masked_lm_array("""embedding_table"""    )

				# Pooling
				UpperCamelCase_:				Any						=    BertPooler(config=lowerCamelCase    )
				UpperCamelCase_:				BertPooler						=    get_encoder_array("""_pooler_layer/kernel"""    )
				UpperCamelCase_:				BertPooler						=    get_encoder_array("""_pooler_layer/bias"""    )

				# Export final model
				model.save_pretrained(lowerCamelCase    )

				# Integration test - should load without any errors ;)
				UpperCamelCase_:				Optional[int]						=    BertForMaskedLM.from_pretrained(lowerCamelCase    )
				print(new_model.eval()    )

				print("""Model conversion was done sucessfully!"""    )


if __name__ == "__main__":
							lowerCamelCase_    :			Any    =	argparse.ArgumentParser()
							parser.add_argument(
							    """--tf_checkpoint_path""", type=str, required=True, help="""Path to the TensorFlow Token Dropping checkpoint path."""
							)
							parser.add_argument(
							    """--bert_config_file""",
							    type=str,
							    required=True,
							    help="""The config json file corresponding to the BERT model. This specifies the model architecture.""",
							)
							parser.add_argument(
							    """--pytorch_dump_path""",
							    type=str,
							    required=True,
							    help="""Path to the output PyTorch model.""",
							)
							lowerCamelCase_    :			Dict    =	parser.parse_args()
							convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)








import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin

@skip_mps
class 							_UpperCamelCase					(       _A  ,							_A  ,							unittest.TestCase	):






			'''simple docstring'''

			__UpperCamelCase   :    str          =			IFInpaintingSuperResolutionPipeline
			__UpperCamelCase   :    str          =			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
			__UpperCamelCase   :    Optional[Any]          =			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""}	)
			__UpperCamelCase   :    Dict          =			PipelineTesterMixin.required_optional_params - {"""latents"""}

			def    lowerCAmelCase__						(							self			:							Union[str, Any]   ):
							return self._get_superresolution_dummy_components()

			def    lowerCAmelCase__						(							self			:							Dict    ,						snake_case_			:							Optional[int]    ,						snake_case_			:							str=0   ):
							if str(snake_case_   ).startswith("""mps"""   ):
											UpperCamelCase_:				Union[str, Any]						=    torch.manual_seed(snake_case_   )
							else:
											UpperCamelCase_:				Tuple						=    torch.Generator(device=snake_case_   ).manual_seed(snake_case_   )

							UpperCamelCase_:				Dict						=    floats_tensor((1, 3, 16, 16)    ,						rng=random.Random(snake_case_   )   ).to(snake_case_   )
							UpperCamelCase_:				int						=    floats_tensor((1, 3, 32, 32)    ,						rng=random.Random(snake_case_   )   ).to(snake_case_   )
							UpperCamelCase_:				str						=    floats_tensor((1, 3, 32, 32)    ,						rng=random.Random(snake_case_   )   ).to(snake_case_   )

							UpperCamelCase_:				Dict						=    {
							    """prompt""": """A painting of a squirrel eating a burger""",
							    """image""": image,
							    """original_image""": original_image,
							    """mask_image""": mask_image,
							    """generator""": generator,
							    """num_inference_steps""": 2,
							    """output_type""": """numpy""",
							}

							return inputs

			@unittest.skipIf(
			    torch_device != """cuda""" or not is_xformers_available()    ,						reason="""XFormers attention is only available with CUDA and `xformers` installed"""    ,						)
			def    lowerCAmelCase__						(							self			:							Tuple   ):
							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3   )

			def    lowerCAmelCase__						(							self			:							Union[str, Any]   ):
							self._test_save_load_optional_components()

			@unittest.skipIf(torch_device != """cuda"""    ,						reason="""float16 requires CUDA"""   )
			def    lowerCAmelCase__						(							self			:							List[Any]   ):
							# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
							super().test_save_load_floataa(expected_max_diff=1e-1   )

			def    lowerCAmelCase__						(							self			:							str   ):
							self._test_attention_slicing_forward_pass(expected_max_diff=1e-2   )

			def    lowerCAmelCase__						(							self			:							str   ):
							self._test_save_load_local()




			def    lowerCAmelCase__						(							self			:							Union[str, Any]   ):
							self._test_inference_batch_single_identical(
							    expected_max_diff=1e-2    ,						)






